Doctor of Philosophy, California Institute of Technology (2011)
Doctor of Medicine, University of California Los Angeles (2013)
Bachelor of Science, Massachusetts Institute of Technology (2003)
Markus Covert, Postdoctoral Faculty Sponsor
Measuring Signaling and RNA-Seq in the Same Cell Links Gene Expression to Dynamic Patterns of NF-?B Activation.
2017; 4 (4): 458-469 e5
Signaling proteins display remarkable cell-to-cell heterogeneity in their dynamic responses to stimuli, but the consequences of this heterogeneity remain largely unknown. For instance, the contribution of the dynamics of the innate immune transcription factor nuclear factor κB (NF-κB) to gene expression output is disputed. Here we explore these questions by integrating live-cell imaging approaches with single-cell sequencing technologies. We used this approach to measure both the dynamics of lipopolysaccharide-induced NF-κB activation and the global transcriptional response in the same individual cell. Our results identify multiple, distinct cytokine expression patterns that are correlated with NF-κB activation dynamics, establishing a functional role for NF-κB dynamics in determining cellular phenotypes. Applications of this approach to other model systems and single-cell sequencing technologies have significant potential for discovery, as it is now possible to trace cellular behavior from the initial stimulus, through the signaling pathways, down to genome-wide changes in gene expression, all inside of a single cell.
View details for DOI 10.1016/j.cels.2017.03.010
View details for PubMedID 28396000
Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments.
PLoS computational biology
2016; 12 (11)
Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.
View details for DOI 10.1371/journal.pcbi.1005177
View details for PubMedID 27814364
View details for PubMedCentralID PMC5096676
A Single-Molecule Hershey-Chase Experiment
2012; 22 (14): 1339-1343
Ever since Hershey and Chase used phages to establish DNA as the carrier of genetic information in 1952, the precise mechanisms of phage DNA translocation have been a mystery. Although bulk measurements have set a timescale for in vivo DNA translocation during bacteriophage infection, measurements of DNA ejection by single bacteriophages have only been made in vitro. Here, we present direct visualization of single bacteriophages infecting individual Escherichia coli cells. For bacteriophage λ, we establish a mean ejection time of roughly 5 min with significant cell-to-cell variability, including pausing events. In contrast, corresponding in vitro single-molecule ejections are more uniform and finish within 10 s. Our data reveal that when plotted against the amount of DNA ejected, the velocity of ejection for two different genome lengths collapses onto a single curve. This suggests that in vivo ejections are controlled by the amount of DNA ejected. In contrast, in vitro DNA ejections are governed by the amount of DNA left inside the capsid. This analysis provides evidence against a purely intrastrand repulsion-based mechanism and suggests that cell-internal processes dominate. This provides a picture of the early stages of phage infection and sheds light on the problem of polymer translocation.
View details for DOI 10.1016/j.cub.2012.05.023
View details for Web of Science ID 000306823700025
View details for PubMedID 22727695
Ion-Dependent Dynamics of DNA Ejections for Bacteriophage lambda
2010; 99 (4): 1101-1109
We studied the control parameters that govern the dynamics of in vitro DNA ejection in bacteriophage lambda. Previous work demonstrated that bacteriophage DNA is highly pressurized, and this pressure has been hypothesized to help drive DNA ejection. Ions influence this process by screening charges on DNA; however, a systematic variation of salt concentrations to explore these effects has not been undertaken. To study the nature of the forces driving DNA ejection, we performed in vitro measurements of DNA ejection in bulk and at the single-phage level. We present measurements on the dynamics of ejection and on the self-repulsion force driving ejection. We examine the role of ion concentration and identity in both measurements, and show that the charge of counterions is an important control parameter. These measurements show that the mobility of ejecting DNA is independent of ionic concentrations for a given amount of DNA in the capsid. We also present evidence that phage DNA forms loops during ejection, and confirm that this effect occurs using optical tweezers.
View details for DOI 10.1016/j.bpj.2010.06.024
View details for Web of Science ID 000281103200013
View details for PubMedID 20712993
Biochemistry on a Leash: The Roles of Tether Length and Geometry in Signal Integration Proteins
2009; 96 (4): 1275-1292
We use statistical mechanics and simple ideas from polymer physics to develop a quantitative model of proteins whose activity is controlled by flexibly tethered ligands and receptors. We predict how the properties of tethers influence the function of these proteins and demonstrate how their tether length dependence can be exploited to construct proteins whose integration of multiple signals can be tuned. One case study to which we apply these ideas is that of the Wiskott-Aldrich Syndrome Proteins as activators of actin polymerization. More generally, tethered ligands competing with those free in solution are common phenomena in biology, making this an important specific example of a widespread biological idea.
View details for DOI 10.1016/j.bpj.2008.10.052
View details for Web of Science ID 000266377800006
View details for PubMedID 19217847